4 papers · 1 filter
Walking the Score Manifold: Continuous-time Generative Dynamics on Learned Data Manifolds
Jan Tauberschmidt, Brian B. Moser, Stanislav Frolov +3
Generative modeling of time-dependent data is typically formulated on a discrete temporal grid, restricting supervision to the observed timestamps in the training data. We instead…
Soft Specialists: -Rényi Ensembles for Uncertainty-Aware LLM Post-Training
Paula Cordero-Encinar, Georgy Tyukin, Andrew B. Duncan
Existing training approaches for large language models learn a single set of parameters, based on large volumes of data, which is typically heterogeneous, conflicting and often out…
Batch-based Bayesian Optimal Experimental Design in Linear Inverse Problems
Sofia Mäkinen, Andrew B. Duncan, Tapio Helin
Experimental design is central to science and engineering. A ubiquitous challenge is how to maximize the value of information obtained from expensive or constrained experimental se…
Diffusion Path Samplers via Sequential Monte Carlo
James Matthew Young, Paula Cordero-Encinar, Sebastian Reich +2
We develop diffusion-based samplers for target distributions known up to a normalising constant. To this end, we rely on the well-known diffusion path that smoothly interpolates be…